{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('x: ', (2, 120))\n",
      "('w: ', (120, 3))\n",
      "('out: ', array([[ 1.49834967,  1.70660132,  1.91485297],\n",
      "       [ 3.25553199,  3.5141327 ,  3.77273342]]))\n",
      "Testing affine_forward function:\n",
      "difference:  9.76984946819e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('db_num: ', array([-5.78588657, -2.14288946, -3.93648137, -4.10664587, -0.09253319]))\n",
      "('x: ', (10, 2, 3))\n",
      "('w: ', (6, 5))\n",
      "('b: ', (5,))\n",
      "('dx: ', (10, 2, 3))\n",
      "('dw: ', (6, 5))\n",
      "('db: ', array([[-5.78588657, -2.14288946, -3.93648137, -4.10664587, -0.09253319]]))\n",
      "Testing affine_backward function:\n",
      "dx error:  5.39910036865e-11\n",
      "dw error:  9.9042118654e-11\n",
      "db error:  2.41228675681e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print(('db_num: ', db_num))\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('out: ', array([[ 0.        ,  0.        ,  0.        ,  0.        ],\n",
      "       [ 0.        ,  0.        ,  0.04545455,  0.13636364],\n",
      "       [ 0.22727273,  0.31818182,  0.40909091,  0.5       ]]))\n",
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27563491363e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "#print(('dx_num: ', dx_num))\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('x: ', (2, 3, 4))\n",
      "('w: ', (12, 10))\n",
      "('b: ', (10,))\n",
      "('dx: ', (2, 3, 4))\n",
      "('dw: ', (12, 10))\n",
      "('db: ', array([[ 0.        , -1.0141586 , -2.30296116,  0.        ,  0.98687019,\n",
      "         0.        ,  0.119619  ,  2.13935149, -0.93426589, -0.42011785]]))\n",
      "Testing affine_relu_forward:\n",
      "dx error:  2.29957917731e-11\n",
      "dw error:  8.16201110576e-11\n",
      "db error:  7.82672402146e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  0.999548019337\n",
      "dx error:  3.27562976742e-12\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  0.692921298947\n",
      "dx error:  1.39717707584e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 2, 3\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "('loss no reg: ', 3.47022435559539)\n",
      "('loss with reg 1: ', 26.594842695238583)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.12e-10\n",
      "b1 relative error: 9.83e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "print(('loss no reg: ', loss))\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "print(('loss with reg 1: ', loss))\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('x_train is: ', array([[2, 1],\n",
      "       [1, 1],\n",
      "       [1, 2],\n",
      "       ..., \n",
      "       [2, 1],\n",
      "       [1, 2],\n",
      "       [2, 1]]))\n",
      "('y_train is: ', array([2, 1, 2, ..., 2, 2, 2]))\n",
      "(Iteration 1 / 450) loss: 1.386305\n",
      "(Epoch 0 / 10) train acc: 0.484000; val_acc: 0.506800\n",
      "(Epoch 1 / 10) train acc: 0.513000; val_acc: 0.506800\n",
      "(Epoch 2 / 10) train acc: 0.487000; val_acc: 0.506800\n",
      "(Iteration 101 / 450) loss: 1.375793\n",
      "(Epoch 3 / 10) train acc: 0.495000; val_acc: 0.506800\n",
      "(Epoch 4 / 10) train acc: 0.512000; val_acc: 0.506800\n",
      "(Iteration 201 / 450) loss: 1.370830\n",
      "(Epoch 5 / 10) train acc: 0.494000; val_acc: 0.506800\n",
      "(Epoch 6 / 10) train acc: 0.497000; val_acc: 0.506800\n",
      "(Iteration 301 / 450) loss: 1.367267\n",
      "(Epoch 7 / 10) train acc: 0.518000; val_acc: 0.506800\n",
      "(Epoch 8 / 10) train acc: 0.525000; val_acc: 0.506800\n",
      "(Iteration 401 / 450) loss: 1.366375\n",
      "(Epoch 9 / 10) train acc: 0.487000; val_acc: 0.506800\n",
      "(Epoch 10 / 10) train acc: 0.503000; val_acc: 0.506800\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "N, D, H, C = 50000, 2, 10, 4\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "X = np.random.randint(1, 3, 100000).reshape(D, N).T\n",
    "y = np.asarray([0, 5, 1])\n",
    "y = np.sum(X, axis = 1) - 1\n",
    "#print(('y label is: ', y[y > 4]))\n",
    "#print(('y label is: ', y))\n",
    "#print(('y label is: ', np.around(y)))\n",
    "x_val = X[:5000, :]\n",
    "y_val = y[:5000]\n",
    "x_train = X[5000:, :]\n",
    "print(('x_train is: ', x_train))\n",
    "y_train = y[5000:]\n",
    "print(('y_train is: ', y_train))\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "model.reg = 0.5\n",
    "data = {\n",
    "  'X_train' : x_train,\n",
    "  'y_train' : y_train,\n",
    "  'X_val' : x_val,\n",
    "  'y_val' : y_val\n",
    "}\n",
    "\n",
    "#data = get_CIFAR10_data()\n",
    "    \n",
    "solver = Solver(model, data, update_rule='sgd',\n",
    "                    optim_config={\n",
    "                      'learning_rate': 1e-3,\n",
    "                    },\n",
    "                    lr_decay=0.75,\n",
    "                    num_epochs=10, batch_size=1000,\n",
    "                    print_every=100)\n",
    "\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YOgAAAJARgR46ym/MUh4tySSNlooqFpKHqzcj4AMAAADSo3QTHTcxVl5VejkzV9Gtd5/W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BSf5+dnmDQigf9YsI31B48yOEHzoLxFxvYC5qzP5cHvdCe1Szun44gH+cV1vVi8/TCPz9vCoskj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\n6ImIiIiIiPiY/wcF3R/ImIfkRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d4e82ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30047908977\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.05211477653\n",
      "W1 relative error: 1.14e-08\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 3.48e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.80e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "\n",
    "data = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.363364\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.108000\n",
      "(Epoch 1 / 20) train acc: 0.320000; val_acc: 0.127000\n",
      "(Epoch 2 / 20) train acc: 0.440000; val_acc: 0.172000\n",
      "(Epoch 3 / 20) train acc: 0.500000; val_acc: 0.184000\n",
      "(Epoch 4 / 20) train acc: 0.540000; val_acc: 0.181000\n",
      "(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.190000\n",
      "(Iteration 11 / 40) loss: 0.839976\n",
      "(Epoch 6 / 20) train acc: 0.740000; val_acc: 0.187000\n",
      "(Epoch 7 / 20) train acc: 0.740000; val_acc: 0.183000\n",
      "(Epoch 8 / 20) train acc: 0.820000; val_acc: 0.177000\n",
      "(Epoch 9 / 20) train acc: 0.860000; val_acc: 0.200000\n",
      "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.191000\n",
      "(Iteration 21 / 40) loss: 0.337174\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.189000\n",
      "(Epoch 12 / 20) train acc: 0.940000; val_acc: 0.180000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.199000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.199000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.195000\n",
      "(Iteration 31 / 40) loss: 0.075911\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.182000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.201000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.207000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.185000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.192000\n"
     ]
    },
    {
     "data": {
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5cOh4Fk6fSZLMn1rIgUPHk0QoA4A10GXJBTt45MTtYWzRwukzOXjkRKeKAGBrEci4YCdP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\nAtDdKKv72xGAaSK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1f3f3f1f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 1e-2\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 166.501707\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.116000\n",
      "(Epoch 1 / 20) train acc: 0.240000; val_acc: 0.083000\n",
      "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.104000\n",
      "(Epoch 3 / 20) train acc: 0.520000; val_acc: 0.106000\n",
      "(Epoch 4 / 20) train acc: 0.700000; val_acc: 0.131000\n",
      "(Epoch 5 / 20) train acc: 0.700000; val_acc: 0.116000\n",
      "(Iteration 11 / 40) loss: 4.414592\n",
      "(Epoch 6 / 20) train acc: 0.840000; val_acc: 0.114000\n",
      "(Epoch 7 / 20) train acc: 0.880000; val_acc: 0.108000\n",
      "(Epoch 8 / 20) train acc: 0.900000; val_acc: 0.109000\n",
      "(Epoch 9 / 20) train acc: 0.960000; val_acc: 0.114000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.127000\n",
      "(Iteration 21 / 40) loss: 0.261098\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Iteration 31 / 40) loss: 0.000594\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.125000\n"
     ]
    },
    {
     "data": {
      "image/png": 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JUmMMaJIkSY0xoEmSJDXGgCZJktQYA5okSVJjDGiSJEmNMaBJkiQ1xoAmSZLUGAOaJElS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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1f3f3f1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-3\n",
    "weight_scale = 1e-1\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[FILL THIS IN]Yes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.559978\n",
      "(Epoch 0 / 5) train acc: 0.103000; val_acc: 0.108000\n",
      "(Iteration 11 / 200) loss: 2.291086\n",
      "(Iteration 21 / 200) loss: 2.153591\n",
      "(Iteration 31 / 200) loss: 2.082693\n",
      "(Epoch 1 / 5) train acc: 0.277000; val_acc: 0.242000\n",
      "(Iteration 41 / 200) loss: 2.004171\n",
      "(Iteration 51 / 200) loss: 2.010409\n",
      "(Iteration 61 / 200) loss: 2.023753\n",
      "(Iteration 71 / 200) loss: 2.026621\n",
      "(Epoch 2 / 5) train acc: 0.352000; val_acc: 0.312000\n",
      "(Iteration 81 / 200) loss: 1.807164\n",
      "(Iteration 91 / 200) loss: 1.915726\n",
      "(Iteration 101 / 200) loss: 1.921844\n",
      "(Iteration 111 / 200) loss: 1.708426\n",
      "(Epoch 3 / 5) train acc: 0.399000; val_acc: 0.318000\n",
      "(Iteration 121 / 200) loss: 1.699757\n",
      "(Iteration 131 / 200) loss: 1.771244\n",
      "(Iteration 141 / 200) loss: 1.790083\n",
      "(Iteration 151 / 200) loss: 1.820212\n",
      "(Epoch 4 / 5) train acc: 0.437000; val_acc: 0.324000\n",
      "(Iteration 161 / 200) loss: 1.630516\n",
      "(Iteration 171 / 200) loss: 1.899523\n",
      "(Iteration 181 / 200) loss: 1.545044\n",
      "(Iteration 191 / 200) loss: 1.700869\n",
      "(Epoch 5 / 5) train acc: 0.436000; val_acc: 0.326000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 3.153777\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.093000\n",
      "(Iteration 11 / 200) loss: 2.145874\n",
      "(Iteration 21 / 200) loss: 2.032563\n",
      "(Iteration 31 / 200) loss: 1.985848\n",
      "(Epoch 1 / 5) train acc: 0.311000; val_acc: 0.281000\n",
      "(Iteration 41 / 200) loss: 1.882353\n",
      "(Iteration 51 / 200) loss: 1.855372\n",
      "(Iteration 61 / 200) loss: 1.649133\n",
      "(Iteration 71 / 200) loss: 1.806432\n",
      "(Epoch 2 / 5) train acc: 0.415000; val_acc: 0.324000\n",
      "(Iteration 81 / 200) loss: 1.907840\n",
      "(Iteration 91 / 200) loss: 1.510681\n",
      "(Iteration 101 / 200) loss: 1.546872\n",
      "(Iteration 111 / 200) loss: 1.512047\n",
      "(Epoch 3 / 5) train acc: 0.434000; val_acc: 0.321000\n",
      "(Iteration 121 / 200) loss: 1.677301\n",
      "(Iteration 131 / 200) loss: 1.504686\n",
      "(Iteration 141 / 200) loss: 1.633253\n",
      "(Iteration 151 / 200) loss: 1.745081\n",
      "(Epoch 4 / 5) train acc: 0.460000; val_acc: 0.353000\n",
      "(Iteration 161 / 200) loss: 1.485411\n",
      "(Iteration 171 / 200) loss: 1.610416\n",
      "(Iteration 181 / 200) loss: 1.528331\n",
      "(Iteration 191 / 200) loss: 1.449159\n",
      "(Epoch 5 / 5) train acc: 0.507000; val_acc: 0.384000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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6GoDTAcwUkS9Z1vk2gA9U9bMAVgC4BwBE5PMArgAwFsBMAA+ISFlUjSciIiIiIurPPAM6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gPVfOuRlby+bg8XzhzLjxbNYGhGanQu7g4fPgFPfxcOlMHxX4JP3xGEOhERERGRKFCgk0Hr5bU7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\nkiSlqP8HoF5D6a7jM1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1f37431c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.40098988 -0.34840634 -0.29582359 -0.24324168 -0.19066062]\n",
      " [-0.13808045 -0.08550121 -0.03292292  0.01965437  0.07223062]\n",
      " [ 0.1248058   0.17737986  0.22995275  0.28252443  0.33509483]\n",
      " [ 0.38766391  0.44023161  0.49279785  0.54536258  0.59792572]]\n",
      "next_w error:  0.00152184517579\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print(next_w)\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.617311\n",
      "(Epoch 0 / 5) train acc: 0.146000; val_acc: 0.116000\n",
      "(Iteration 11 / 200) loss: 2.170862\n",
      "(Iteration 21 / 200) loss: 1.938017\n",
      "(Iteration 31 / 200) loss: 1.829561\n",
      "(Epoch 1 / 5) train acc: 0.372000; val_acc: 0.328000\n",
      "(Iteration 41 / 200) loss: 1.804995\n",
      "(Iteration 51 / 200) loss: 1.466055\n",
      "(Iteration 61 / 200) loss: 1.633346\n",
      "(Iteration 71 / 200) loss: 1.674702\n",
      "(Epoch 2 / 5) train acc: 0.424000; val_acc: 0.359000\n",
      "(Iteration 81 / 200) loss: 1.605404\n",
      "(Iteration 91 / 200) loss: 1.581030\n",
      "(Iteration 101 / 200) loss: 1.561559\n",
      "(Iteration 111 / 200) loss: 1.489700\n",
      "(Epoch 3 / 5) train acc: 0.521000; val_acc: 0.377000\n",
      "(Iteration 121 / 200) loss: 1.410764\n",
      "(Iteration 131 / 200) loss: 1.337014\n",
      "(Iteration 141 / 200) loss: 1.331301\n",
      "(Iteration 151 / 200) loss: 1.377437\n",
      "(Epoch 4 / 5) train acc: 0.551000; val_acc: 0.395000\n",
      "(Iteration 161 / 200) loss: 1.225887\n",
      "(Iteration 171 / 200) loss: 1.414174\n",
      "(Iteration 181 / 200) loss: 1.203719\n",
      "(Iteration 191 / 200) loss: 1.021116\n",
      "(Epoch 5 / 5) train acc: 0.625000; val_acc: 0.394000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.588805\n",
      "(Epoch 0 / 5) train acc: 0.125000; val_acc: 0.127000\n",
      "(Iteration 11 / 200) loss: 2.030611\n",
      "(Iteration 21 / 200) loss: 1.921604\n",
      "(Iteration 31 / 200) loss: 1.843213\n",
      "(Epoch 1 / 5) train acc: 0.378000; val_acc: 0.302000\n",
      "(Iteration 41 / 200) loss: 1.795204\n",
      "(Iteration 51 / 200) loss: 1.882677\n",
      "(Iteration 61 / 200) loss: 1.725109\n",
      "(Iteration 71 / 200) loss: 1.742972\n",
      "(Epoch 2 / 5) train acc: 0.445000; val_acc: 0.329000\n",
      "(Iteration 81 / 200) loss: 1.575235\n",
      "(Iteration 91 / 200) loss: 1.493222\n",
      "(Iteration 101 / 200) loss: 1.658173\n",
      "(Iteration 111 / 200) loss: 1.595397\n",
      "(Epoch 3 / 5) train acc: 0.449000; val_acc: 0.347000\n",
      "(Iteration 121 / 200) loss: 1.501724\n",
      "(Iteration 131 / 200) loss: 1.558119\n",
      "(Iteration 141 / 200) loss: 1.412844\n",
      "(Iteration 151 / 200) loss: 1.536252\n",
      "(Epoch 4 / 5) train acc: 0.522000; val_acc: 0.364000\n",
      "(Iteration 161 / 200) loss: 1.415521\n",
      "(Iteration 171 / 200) loss: 1.575067\n",
      "(Iteration 181 / 200) loss: 1.228684\n",
      "(Iteration 191 / 200) loss: 1.460314\n",
      "(Epoch 5 / 5) train acc: 0.548000; val_acc: 0.366000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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fP55FixYxffp0Kisrqa2t5f3vfz8zZsxg/Pjxsf1ra2v55Cc/ybRp0xgyZEihl9WtumMZ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\nM3j+fgjF3k90n9Fvw5wKdZMlgU4IMSf4QxFOtPdR2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qt0kC5dOiGBkADpuff8/pgbUgmB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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d50a19cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 2450) loss: 2.576806\n",
      "(Epoch 0 / 5) train acc: 0.125000; val_acc: 0.129000\n",
      "(Iteration 201 / 2450) loss: 1.583363\n",
      "(Iteration 401 / 2450) loss: 1.619887\n",
      "(Epoch 1 / 5) train acc: 0.440000; val_acc: 0.455000\n",
      "(Iteration 601 / 2450) loss: 1.449479\n",
      "(Iteration 801 / 2450) loss: 1.296520\n",
      "(Epoch 2 / 5) train acc: 0.542000; val_acc: 0.481000\n",
      "(Iteration 1001 / 2450) loss: 1.455361\n",
      "(Iteration 1201 / 2450) loss: 1.382140\n",
      "(Iteration 1401 / 2450) loss: 1.393709\n",
      "(Epoch 3 / 5) train acc: 0.525000; val_acc: 0.477000\n",
      "(Iteration 1601 / 2450) loss: 1.367862\n",
      "(Iteration 1801 / 2450) loss: 1.218762\n",
      "(Epoch 4 / 5) train acc: 0.543000; val_acc: 0.487000\n",
      "(Iteration 2001 / 2450) loss: 1.421003\n",
      "(Iteration 2201 / 2450) loss: 1.197627\n",
      "(Iteration 2401 / 2450) loss: 1.181193\n",
      "(Epoch 5 / 5) train acc: 0.566000; val_acc: 0.499000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 2450) loss: 2.679344\n",
      "(Epoch 0 / 5) train acc: 0.122000; val_acc: 0.121000\n",
      "(Iteration 201 / 2450) loss: 1.632191\n",
      "(Iteration 401 / 2450) loss: 1.743796\n",
      "(Epoch 1 / 5) train acc: 0.440000; val_acc: 0.431000\n",
      "(Iteration 601 / 2450) loss: 1.627368\n",
      "(Iteration 801 / 2450) loss: 1.505892\n",
      "(Epoch 2 / 5) train acc: 0.465000; val_acc: 0.462000\n",
      "(Iteration 1001 / 2450) loss: 1.511033\n",
      "(Iteration 1201 / 2450) loss: 1.441558\n",
      "(Iteration 1401 / 2450) loss: 1.546501\n",
      "(Epoch 3 / 5) train acc: 0.473000; val_acc: 0.476000\n",
      "(Iteration 1601 / 2450) loss: 1.379096\n",
      "(Iteration 1801 / 2450) loss: 1.396062\n",
      "(Epoch 4 / 5) train acc: 0.531000; val_acc: 0.477000\n",
      "(Iteration 2001 / 2450) loss: 1.439652\n",
      "(Iteration 2201 / 2450) loss: 1.461662\n",
      "(Iteration 2401 / 2450) loss: 1.436317\n",
      "(Epoch 5 / 5) train acc: 0.549000; val_acc: 0.488000\n",
      "\n",
      "('Current best is: ', 0)\n",
      "('Update best solver: ', 'rmsprop')\n",
      "('Current best is: ', 'rmsprop')\n",
      "('Update best solver: ', 'adam')\n"
     ]
    },
    {
     "data": {
      "image/png": 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FnfeKK64gFApx3nnnsWjRImbMmMHo0aOpra3l/e9/PzNnzuS8886L719bW8vHP/5xpk2b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\n6s38X3won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sSwUF9fn3QhlbVr15Kbm5uCHqXWUPl7q5SiLdyGJ+jBE/DgDXppCbbgDUS+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9lv+0nPzm+V4ACwtl4aGrvrRmTVk+hftmjsTndrQOxlsi9x83Oqo9ERY/txUepCr73FaFtxGW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\nREREmigFOhERERERkSbq/wHUhR99VUT6rAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d4e824358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  print_every=200,\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "bestAcc = 0\n",
    "bestSolver = 0\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  if bestAcc < solver.val_acc_history[4]:\n",
    "        bestAcc = solver.val_acc_history[4]\n",
    "        print(('Current best is: ', bestSolver))\n",
    "        bestSolver = update_rule\n",
    "        print(('Update best solver: ', bestSolver))\n",
    "\n",
    "best_model = solvers[bestSolver].model\n",
    "\n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.499\n",
      "Test set accuracy:  0.498\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
